Smooth Hazards With Multiple Time Scales
Statistics in Medicine, vol. 44, pp. e10297
Abstract
ABSTRACT Hazard models are the most commonly used tool to analyze time‐to‐event data. If more than one time scale is relevant for the event under study, models are required that can incorporate the dependence of a hazard along two (or more) time scales. Such models should be flexible to capture the joint influence of several time scales, and nonparametric smoothing techniques are obvious candidates. ‐splines offer a flexible way to specify such hazard surfaces, and estimation is achieved by maximizing a penalized Poisson likelihood. Standard observation schemes, such as right‐censoring and left‐truncation, can be accommodated in a straightforward manner. Proportional hazards regression with a baseline hazard varying over two time scales is presented. Efficient computation is possible by generalized linear array model (GLAM) algorithms or by exploiting a sparse mixed model formulation. A companion R‐package is provided.
Authors 4
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Angela Carollo Aachen Department of Biomedical Data Sciences Leiden University Medical Center Leiden The Netherlands Department of Biomedical Data Sciences
Leiden University Medical Center · Max Planck Institute for Demographic Research
Affiliation as printed
Department of Biomedical Data Sciences Leiden University Medical Center Leiden The Netherlands
Max Planck Institute for Demographic Research Rostock Germany
Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
Max Planck Institute for Demographic Research, Rostock, Germany
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Affiliation as printed
Erasmus University Medical Center Rotterdam The Netherlands
Erasmus University Medical Center, Rotterdam, The Netherlands
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Hein Putter Aachen Department of Biomedical Data Sciences Leiden University Medical Center Leiden The Netherlands Department of Biomedical Data Sciences
Leiden University Medical Center
Affiliation as printed
Department of Biomedical Data Sciences Leiden University Medical Center Leiden The Netherlands
Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
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Jutta Gampe corresponding
Max Planck Institute for Demographic Research
Affiliation as printed
Max Planck Institute for Demographic Research Rostock Germany
Max Planck Institute for Demographic Research, Rostock, Germany
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